Instructions to use AppliedMLReedShreya/ViT_Attempt_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AppliedMLReedShreya/ViT_Attempt_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="AppliedMLReedShreya/ViT_Attempt_1") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("AppliedMLReedShreya/ViT_Attempt_1") model = AutoModelForImageClassification.from_pretrained("AppliedMLReedShreya/ViT_Attempt_1") - Notebooks
- Google Colab
- Kaggle
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This is a fine-tuned Vision Transformer (ViT) model from Google. The model was loaded and fine-tuned on the training data collected.
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# OLD MODEL: DO NOT USE FOR LEADERBOARD
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# Model Card for Model ID
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This is a fine-tuned Vision Transformer (ViT) model from Google. The model was loaded and fine-tuned on the training data collected.
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